{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# -*- coding:utf-8 -*-\n",
    "import sys\n",
    "#reload(sys)\n",
    "#sys.setdefaultencoding(\"utf-8\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "      <th>MEDV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00632</td>\n",
       "      <td>18</td>\n",
       "      <td>2.31</td>\n",
       "      <td>0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>6.575</td>\n",
       "      <td>65.2</td>\n",
       "      <td>4.0900</td>\n",
       "      <td>1</td>\n",
       "      <td>296</td>\n",
       "      <td>15</td>\n",
       "      <td>396.90</td>\n",
       "      <td>4.98</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.02731</td>\n",
       "      <td>0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>6.421</td>\n",
       "      <td>78.9</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2</td>\n",
       "      <td>242</td>\n",
       "      <td>17</td>\n",
       "      <td>396.90</td>\n",
       "      <td>9.14</td>\n",
       "      <td>21.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.02729</td>\n",
       "      <td>0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>7.185</td>\n",
       "      <td>61.1</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2</td>\n",
       "      <td>242</td>\n",
       "      <td>17</td>\n",
       "      <td>392.83</td>\n",
       "      <td>4.03</td>\n",
       "      <td>34.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.03237</td>\n",
       "      <td>0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>6.998</td>\n",
       "      <td>45.8</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3</td>\n",
       "      <td>222</td>\n",
       "      <td>18</td>\n",
       "      <td>394.63</td>\n",
       "      <td>2.94</td>\n",
       "      <td>33.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.06905</td>\n",
       "      <td>0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>7.147</td>\n",
       "      <td>54.2</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3</td>\n",
       "      <td>222</td>\n",
       "      <td>18</td>\n",
       "      <td>396.90</td>\n",
       "      <td>5.33</td>\n",
       "      <td>36.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      CRIM  ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD  TAX  PTRATIO  \\\n",
       "0  0.00632  18   2.31     0  0.538  6.575  65.2  4.0900    1  296       15   \n",
       "1  0.02731   0   7.07     0  0.469  6.421  78.9  4.9671    2  242       17   \n",
       "2  0.02729   0   7.07     0  0.469  7.185  61.1  4.9671    2  242       17   \n",
       "3  0.03237   0   2.18     0  0.458  6.998  45.8  6.0622    3  222       18   \n",
       "4  0.06905   0   2.18     0  0.458  7.147  54.2  6.0622    3  222       18   \n",
       "\n",
       "        B  LSTAT  MEDV  \n",
       "0  396.90   4.98  24.0  \n",
       "1  396.90   9.14  21.6  \n",
       "2  392.83   4.03  34.7  \n",
       "3  394.63   2.94  33.4  \n",
       "4  396.90   5.33  36.2  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#dpath = \"./data/\"\n",
    "df = pd.read_csv(\"boston_housing.csv\")\n",
    "\n",
    "#显示前5行\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 506 entries, 0 to 505\n",
      "Data columns (total 14 columns):\n",
      " #   Column   Non-Null Count  Dtype  \n",
      "---  ------   --------------  -----  \n",
      " 0   CRIM     506 non-null    float64\n",
      " 1   ZN       506 non-null    int64  \n",
      " 2   INDUS    506 non-null    float64\n",
      " 3   CHAS     506 non-null    int64  \n",
      " 4   NOX      506 non-null    float64\n",
      " 5   RM       506 non-null    float64\n",
      " 6   AGE      506 non-null    float64\n",
      " 7   DIS      506 non-null    float64\n",
      " 8   RAD      506 non-null    int64  \n",
      " 9   TAX      506 non-null    int64  \n",
      " 10  PTRATIO  506 non-null    int64  \n",
      " 11  B        506 non-null    float64\n",
      " 12  LSTAT    506 non-null    float64\n",
      " 13  MEDV     506 non-null    float64\n",
      "dtypes: float64(9), int64(5)\n",
      "memory usage: 55.5 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "第一题：\n",
    "Q：对连续型特征，可以用哪个函数可视化其分布？（给出你最常用的一个即可），并根据代码运行结果给出示例。\n",
    "\n",
    "A：\n",
    "有以下的几种方法：\n",
    "\n",
    "直方图、箱体图、提琴型图。对应如下函数：\n",
    "distplot\n",
    "boxplot\n",
    "violinplot\n",
    "示例如下：\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(df[\"MEDV\"], bins=30, kde=True)\n",
    "plt.xlabel(\"Median value of owner-occupied homes\", fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_, axes = plt.subplots(1, 2, sharey=True, figsize=(6, 4))\n",
    "sns.boxplot(data=df[\"MEDV\"], ax=axes[0]); \n",
    "sns.violinplot(data=df[\"MEDV\"], ax=axes[1]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "第二题：\n",
    "Q：对两个连续型特征，可以用哪个函数得到这两个特征之间的相关性？根据代码运行结果，给出示例。\n",
    "\n",
    "A：两个数值特征之间的关系可用相关矩阵来查看它们之间的相关性。可用DataFrame的corr()方法计算出每对特征间的相关矩阵"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x26ebdfeeb08>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#get the names of all the columns\n",
    "cols = df.columns \n",
    "\n",
    "# Calculates pearson co-efficient for all combinations，通常认为相关系数大于0.5的为强相关\n",
    "data_corr = df.corr()\n",
    "sns.heatmap(data_corr,annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 得到相关系数的绝对值，通常认为相关系数的绝对值大于0.5的特征为强相关\n",
    "data_corr = data_corr.abs()\n",
    "plt.subplots(figsize=(13, 9))\n",
    "sns.heatmap(data_corr,annot=True)\n",
    "\n",
    "# Mask unimportant features,突出重要信息\n",
    "sns.heatmap(data_corr, mask=data_corr < 0.5, cbar=False)\n",
    "\n",
    "#plt.savefig(\"house_coor.png\" )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "第三题：\n",
    "Q：如果发现特征之间有较强的相关性，在选择线性回归模型时应该采取什么措施。\n",
    "\n",
    "A：可考虑进行PCA降维（特征层面）或加正则项（模型层面）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "第四题：\n",
    "Q：当采用带正则的模型以及采用随机梯度下降优化算法时，需要对输入（连续型）特征进行去量纲预处理。课程代码给出了用标准化（StandardScaler）的结果，请改成最小最大缩放（MinMaxScaler）去量纲 ，并重新训练最小二乘线性回归、岭回归、和Lasso模型。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "最小二乘线性回归"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 从原始数据中分离输入特征x和输出y\n",
    "from sklearn.metrics import r2_score  #评价回归预测模型的性能\n",
    "\n",
    "y = df['MEDV']\n",
    "X = df.drop('MEDV', axis = 1)\n",
    "\n",
    "# 尝试对y（房屋价格）做log变换，对log变换后的价格进行估计\n",
    "log_y = np.log1p(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# RAD的含义是距离高速公路的便利指数。虽然给的数值是数值型，但实际是索引，可换成离散特征/类别型特征编码试试。\n",
    "X[\"RAD\"].astype(\"object\")\n",
    "X_cat = X[\"RAD\"]\n",
    "X_cat = pd.get_dummies(X_cat, prefix=\"RAD\")\n",
    "\n",
    "X = X.drop(\"RAD\", axis = 1)\n",
    "\n",
    "#特征名称，用于保存特征工程结果\n",
    "feat_names = X.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 数据标准化\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "# 分别初始化对特征和目标值的标准化器\n",
    "ss_X = MinMaxScaler()\n",
    "ss_y = MinMaxScaler()\n",
    "\n",
    "ss_log_y = MinMaxScaler()\n",
    "# 分别对训练和测试数据的特征以及目标值进行标准化处理\n",
    "# 对训练数据，先调用fit方法训练模型，得到模型参数；然后对训练数据和测试数据进行transform\n",
    "X = ss_X.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "fe_data = pd.DataFrame(data = X, columns = feat_names, index = df.index)\n",
    "fe_data = pd.concat([fe_data, X_cat], axis = 1, ignore_index=False)\n",
    "\n",
    "#加上标签y\n",
    "fe_data[\"MEDV\"] = y\n",
    "fe_data[\"log_MEDV\"] = log_y\n",
    "\n",
    "#保存结果到文件\n",
    "fe_data.to_csv('FE_boston_housing_homework.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>...</th>\n",
       "      <th>RAD_2</th>\n",
       "      <th>RAD_3</th>\n",
       "      <th>RAD_4</th>\n",
       "      <th>RAD_5</th>\n",
       "      <th>RAD_6</th>\n",
       "      <th>RAD_7</th>\n",
       "      <th>RAD_8</th>\n",
       "      <th>RAD_24</th>\n",
       "      <th>MEDV</th>\n",
       "      <th>log_MEDV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.067815</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.314815</td>\n",
       "      <td>0.577505</td>\n",
       "      <td>0.641607</td>\n",
       "      <td>0.269203</td>\n",
       "      <td>0.208015</td>\n",
       "      <td>0.3</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>3.218876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000236</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.242302</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.172840</td>\n",
       "      <td>0.547998</td>\n",
       "      <td>0.782698</td>\n",
       "      <td>0.348962</td>\n",
       "      <td>0.104962</td>\n",
       "      <td>0.5</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>21.6</td>\n",
       "      <td>3.117950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000236</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.242302</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.172840</td>\n",
       "      <td>0.694386</td>\n",
       "      <td>0.599382</td>\n",
       "      <td>0.348962</td>\n",
       "      <td>0.104962</td>\n",
       "      <td>0.5</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>34.7</td>\n",
       "      <td>3.575151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.000293</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.063050</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.150206</td>\n",
       "      <td>0.658555</td>\n",
       "      <td>0.441813</td>\n",
       "      <td>0.448545</td>\n",
       "      <td>0.066794</td>\n",
       "      <td>0.6</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>33.4</td>\n",
       "      <td>3.538057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.000705</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.063050</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.150206</td>\n",
       "      <td>0.687105</td>\n",
       "      <td>0.528321</td>\n",
       "      <td>0.448545</td>\n",
       "      <td>0.066794</td>\n",
       "      <td>0.6</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>36.2</td>\n",
       "      <td>3.616309</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       CRIM    ZN     INDUS  CHAS       NOX        RM       AGE       DIS  \\\n",
       "0  0.000000  0.18  0.067815   0.0  0.314815  0.577505  0.641607  0.269203   \n",
       "1  0.000236  0.00  0.242302   0.0  0.172840  0.547998  0.782698  0.348962   \n",
       "2  0.000236  0.00  0.242302   0.0  0.172840  0.694386  0.599382  0.348962   \n",
       "3  0.000293  0.00  0.063050   0.0  0.150206  0.658555  0.441813  0.448545   \n",
       "4  0.000705  0.00  0.063050   0.0  0.150206  0.687105  0.528321  0.448545   \n",
       "\n",
       "        TAX  PTRATIO  ...  RAD_2  RAD_3  RAD_4  RAD_5  RAD_6  RAD_7  RAD_8  \\\n",
       "0  0.208015      0.3  ...      0      0      0      0      0      0      0   \n",
       "1  0.104962      0.5  ...      1      0      0      0      0      0      0   \n",
       "2  0.104962      0.5  ...      1      0      0      0      0      0      0   \n",
       "3  0.066794      0.6  ...      0      1      0      0      0      0      0   \n",
       "4  0.066794      0.6  ...      0      1      0      0      0      0      0   \n",
       "\n",
       "   RAD_24  MEDV  log_MEDV  \n",
       "0       0  24.0  3.218876  \n",
       "1       0  21.6  3.117950  \n",
       "2       0  34.7  3.575151  \n",
       "3       0  33.4  3.538057  \n",
       "4       0  36.2  3.616309  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# path to where the data lies\n",
    "#dpath = './data/'\n",
    "df = pd.read_csv(\"FE_boston_housing_homework.csv\")\n",
    "\n",
    "#通过观察前5行，了解数据每列（特征）的概况\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r2 score of LinearRegression on test is 0.6939789810509471\n",
      "The r2 score of LinearRegression on train is 0.7549146436868177\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 从原始数据中分离输入特征x和输出y\n",
    "y = df[\"MEDV\"]\n",
    "\n",
    "X = df.drop([\"MEDV\", \"log_MEDV\"], axis = 1)\n",
    "\n",
    "#特征名称，用于后续显示权重系数对应的特征\n",
    "feat_names = X.columns\n",
    "\n",
    "#将数据分割训练数据与测试数据\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# 随机采样20%的数据构建测试样本，其余作为训练样本\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=33, test_size=0.2)\n",
    "X_train.shape\n",
    "\n",
    "# 线性回归\n",
    "#class sklearn.linear_model.LinearRegression(fit_intercept=True, normalize=False, copy_X=True, n_jobs=1)\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "# 1.使用默认配置初始化学习器实例\n",
    "lr = LinearRegression()\n",
    "\n",
    "# 2.用训练数据训练模型参数\n",
    "lr.fit(X_train, y_train)\n",
    "\n",
    "# 3. 用训练好的模型对测试集进行预测\n",
    "y_test_pred_lr = lr.predict(X_test)\n",
    "y_train_pred_lr = lr.predict(X_train)\n",
    "\n",
    "\n",
    "# 看看各特征的权重系数，系数的绝对值大小可视为该特征的重要性\n",
    "fs = pd.DataFrame({\"columns\":list(feat_names), \"coef\":list((lr.coef_.T))})\n",
    "fs.sort_values(by=['coef'],ascending=False)\n",
    "\n",
    "# 使用r2_score评价模型在测试集和训练集上的性能，并输出评估结果\n",
    "#测试集\n",
    "print ('The r2 score of LinearRegression on test is '+str(r2_score(y_test, y_test_pred_lr)))\n",
    "#训练集\n",
    "print ('The r2 score of LinearRegression on train is '+str(r2_score(y_train, y_train_pred_lr)))\n",
    "\n",
    "#在训练集上观察预测残差的分布，看是否符合模型假设：噪声为0均值的高斯噪声\n",
    "f, ax = plt.subplots(figsize=(7, 5)) \n",
    "f.tight_layout() \n",
    "ax.hist(y_train - y_train_pred_lr, bins=40, label='Residuals Linear', color='b', alpha=.5); \n",
    "ax.set_title(\"Histogram of Residuals\") \n",
    "ax.legend(loc='best');\n",
    "\n",
    "plt.figure(figsize=(4, 3))\n",
    "plt.scatter(y_train, y_train_pred_lr)\n",
    "plt.plot([-3, 3], [-3, 3], '--k')   #数据已经标准化，3倍标准差即可\n",
    "plt.axis('tight')\n",
    "plt.xlabel('True price')\n",
    "plt.ylabel('Predicted price')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "岭回归"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r2 score of RidgeCV on test is 0.6961684813070386\n",
      "The r2 score of RidgeCV on train is 0.754852444044556\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import  RidgeCV\n",
    "\n",
    "#1. 设置超参数（正则参数）范围\n",
    "alphas = [ 0.01, 0.1, 1, 10,100]\n",
    "#n_alphas = 20\n",
    "#alphas = np.logspace(-5,2,n_alphas)\n",
    "\n",
    "#2. 生成一个RidgeCV实例\n",
    "ridge = RidgeCV(alphas=alphas, store_cv_values=True)  \n",
    "\n",
    "#3. 模型训练\n",
    "ridge.fit(X_train, y_train)    \n",
    "\n",
    "#4. 预测\n",
    "y_test_pred_ridge = ridge.predict(X_test)\n",
    "y_train_pred_ridge = ridge.predict(X_train)\n",
    "\n",
    "print ('The r2 score of RidgeCV on test is '+str(r2_score(y_test, y_test_pred_ridge)))\n",
    "print ('The r2 score of RidgeCV on train is '+str(r2_score(y_train, y_train_pred_ridge)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "alpha is: 0.1\n"
     ]
    }
   ],
   "source": [
    "mse_mean = np.mean(ridge.cv_values_, axis = 0)\n",
    "plt.plot(np.log10(alphas), mse_mean.reshape(len(alphas),1)) \n",
    "\n",
    "#这是为了标出最佳参数的位置，不是必须\n",
    "#plt.plot(np.log10(ridge.alpha_)*np.ones(3), [0.28, 0.29, 0.30])\n",
    "\n",
    "plt.xlabel('log(alpha)')\n",
    "plt.ylabel('mse')\n",
    "plt.show()\n",
    "\n",
    "print ('alpha is:', ridge.alpha_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>columns</th>\n",
       "      <th>coef_lr</th>\n",
       "      <th>coef_ridge</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>RM</td>\n",
       "      <td>20.356954</td>\n",
       "      <td>20.209673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ZN</td>\n",
       "      <td>5.815739</td>\n",
       "      <td>5.668449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>RAD_24</td>\n",
       "      <td>4.644938</td>\n",
       "      <td>4.495999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>B</td>\n",
       "      <td>3.550457</td>\n",
       "      <td>3.569715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CHAS</td>\n",
       "      <td>2.684704</td>\n",
       "      <td>2.703375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>RAD_7</td>\n",
       "      <td>1.694896</td>\n",
       "      <td>1.652060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>RAD_8</td>\n",
       "      <td>1.353203</td>\n",
       "      <td>1.369027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>RAD_3</td>\n",
       "      <td>1.235936</td>\n",
       "      <td>1.276021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>INDUS</td>\n",
       "      <td>0.621810</td>\n",
       "      <td>0.517086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>AGE</td>\n",
       "      <td>-0.055280</td>\n",
       "      <td>-0.048454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>RAD_4</td>\n",
       "      <td>-0.391399</td>\n",
       "      <td>-0.389052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>RAD_5</td>\n",
       "      <td>-0.518265</td>\n",
       "      <td>-0.503234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>RAD_2</td>\n",
       "      <td>-1.843667</td>\n",
       "      <td>-1.756881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>RAD_6</td>\n",
       "      <td>-2.508106</td>\n",
       "      <td>-2.495217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>RAD_1</td>\n",
       "      <td>-3.667536</td>\n",
       "      <td>-3.648724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>TAX</td>\n",
       "      <td>-5.109656</td>\n",
       "      <td>-4.958641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NOX</td>\n",
       "      <td>-6.819892</td>\n",
       "      <td>-6.565940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>PTRATIO</td>\n",
       "      <td>-8.455428</td>\n",
       "      <td>-8.431607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CRIM</td>\n",
       "      <td>-9.984341</td>\n",
       "      <td>-9.567877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>DIS</td>\n",
       "      <td>-17.381223</td>\n",
       "      <td>-16.868997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>LSTAT</td>\n",
       "      <td>-21.450356</td>\n",
       "      <td>-21.357121</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    columns    coef_lr  coef_ridge\n",
       "5        RM  20.356954   20.209673\n",
       "1        ZN   5.815739    5.668449\n",
       "20   RAD_24   4.644938    4.495999\n",
       "10        B   3.550457    3.569715\n",
       "3      CHAS   2.684704    2.703375\n",
       "18    RAD_7   1.694896    1.652060\n",
       "19    RAD_8   1.353203    1.369027\n",
       "14    RAD_3   1.235936    1.276021\n",
       "2     INDUS   0.621810    0.517086\n",
       "6       AGE  -0.055280   -0.048454\n",
       "15    RAD_4  -0.391399   -0.389052\n",
       "16    RAD_5  -0.518265   -0.503234\n",
       "13    RAD_2  -1.843667   -1.756881\n",
       "17    RAD_6  -2.508106   -2.495217\n",
       "12    RAD_1  -3.667536   -3.648724\n",
       "8       TAX  -5.109656   -4.958641\n",
       "4       NOX  -6.819892   -6.565940\n",
       "9   PTRATIO  -8.455428   -8.431607\n",
       "0      CRIM  -9.984341   -9.567877\n",
       "7       DIS -17.381223  -16.868997\n",
       "11    LSTAT -21.450356  -21.357121"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fs = pd.DataFrame({\"columns\":list(feat_names), \"coef_lr\":list((lr.coef_.T)), \"coef_ridge\":list((ridge.coef_.T))})\n",
    "fs.sort_values(by=['coef_lr'],ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lasso"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r2 score of LassoCV on test is 0.6953719445869473\n",
      "The r2 score of LassoCV on train is 0.7546505272258798\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LassoCV\n",
    "\n",
    "\n",
    "lasso = LassoCV()  \n",
    "\n",
    "lasso.fit(X_train, y_train)  \n",
    "\n",
    "y_test_pred_lasso = lasso.predict(X_test)\n",
    "y_train_pred_lasso = lasso.predict(X_train)\n",
    "\n",
    "print('The r2 score of LassoCV on test is '+str(r2_score(y_test, y_test_pred_lasso)))\n",
    "print('The r2 score of LassoCV on train is '+str(r2_score(y_train, y_train_pred_lasso)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "alpha is: 0.004240023611635234\n"
     ]
    }
   ],
   "source": [
    "mses = np.mean(lasso.mse_path_, axis = 1)\n",
    "plt.plot(np.log10(lasso.alphas_), mses) \n",
    "plt.xlabel('log(alpha)')\n",
    "plt.ylabel('mse')\n",
    "plt.show()    \n",
    "            \n",
    "print ('alpha is:', lasso.alpha_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>columns</th>\n",
       "      <th>coef_lr</th>\n",
       "      <th>coef_ridge</th>\n",
       "      <th>coef_lasso</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>RM</td>\n",
       "      <td>20.356954</td>\n",
       "      <td>20.209673</td>\n",
       "      <td>20.348146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ZN</td>\n",
       "      <td>5.815739</td>\n",
       "      <td>5.668449</td>\n",
       "      <td>5.432681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>RAD_24</td>\n",
       "      <td>4.644938</td>\n",
       "      <td>4.495999</td>\n",
       "      <td>4.461097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>B</td>\n",
       "      <td>3.550457</td>\n",
       "      <td>3.569715</td>\n",
       "      <td>3.482474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CHAS</td>\n",
       "      <td>2.684704</td>\n",
       "      <td>2.703375</td>\n",
       "      <td>2.714761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>RAD_7</td>\n",
       "      <td>1.694896</td>\n",
       "      <td>1.652060</td>\n",
       "      <td>1.881650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>RAD_8</td>\n",
       "      <td>1.353203</td>\n",
       "      <td>1.369027</td>\n",
       "      <td>1.607248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>RAD_3</td>\n",
       "      <td>1.235936</td>\n",
       "      <td>1.276021</td>\n",
       "      <td>1.642010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>INDUS</td>\n",
       "      <td>0.621810</td>\n",
       "      <td>0.517086</td>\n",
       "      <td>0.012894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>AGE</td>\n",
       "      <td>-0.055280</td>\n",
       "      <td>-0.048454</td>\n",
       "      <td>-0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>RAD_4</td>\n",
       "      <td>-0.391399</td>\n",
       "      <td>-0.389052</td>\n",
       "      <td>-0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>RAD_5</td>\n",
       "      <td>-0.518265</td>\n",
       "      <td>-0.503234</td>\n",
       "      <td>-0.122759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>RAD_2</td>\n",
       "      <td>-1.843667</td>\n",
       "      <td>-1.756881</td>\n",
       "      <td>-1.183361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>RAD_6</td>\n",
       "      <td>-2.508106</td>\n",
       "      <td>-2.495217</td>\n",
       "      <td>-2.105539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>RAD_1</td>\n",
       "      <td>-3.667536</td>\n",
       "      <td>-3.648724</td>\n",
       "      <td>-3.152265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>TAX</td>\n",
       "      <td>-5.109656</td>\n",
       "      <td>-4.958641</td>\n",
       "      <td>-4.382396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NOX</td>\n",
       "      <td>-6.819892</td>\n",
       "      <td>-6.565940</td>\n",
       "      <td>-6.232770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>PTRATIO</td>\n",
       "      <td>-8.455428</td>\n",
       "      <td>-8.431607</td>\n",
       "      <td>-8.278664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CRIM</td>\n",
       "      <td>-9.984341</td>\n",
       "      <td>-9.567877</td>\n",
       "      <td>-9.140572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>DIS</td>\n",
       "      <td>-17.381223</td>\n",
       "      <td>-16.868997</td>\n",
       "      <td>-16.616306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>LSTAT</td>\n",
       "      <td>-21.450356</td>\n",
       "      <td>-21.357121</td>\n",
       "      <td>-21.501518</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    columns    coef_lr  coef_ridge  coef_lasso\n",
       "5        RM  20.356954   20.209673   20.348146\n",
       "1        ZN   5.815739    5.668449    5.432681\n",
       "20   RAD_24   4.644938    4.495999    4.461097\n",
       "10        B   3.550457    3.569715    3.482474\n",
       "3      CHAS   2.684704    2.703375    2.714761\n",
       "18    RAD_7   1.694896    1.652060    1.881650\n",
       "19    RAD_8   1.353203    1.369027    1.607248\n",
       "14    RAD_3   1.235936    1.276021    1.642010\n",
       "2     INDUS   0.621810    0.517086    0.012894\n",
       "6       AGE  -0.055280   -0.048454   -0.000000\n",
       "15    RAD_4  -0.391399   -0.389052   -0.000000\n",
       "16    RAD_5  -0.518265   -0.503234   -0.122759\n",
       "13    RAD_2  -1.843667   -1.756881   -1.183361\n",
       "17    RAD_6  -2.508106   -2.495217   -2.105539\n",
       "12    RAD_1  -3.667536   -3.648724   -3.152265\n",
       "8       TAX  -5.109656   -4.958641   -4.382396\n",
       "4       NOX  -6.819892   -6.565940   -6.232770\n",
       "9   PTRATIO  -8.455428   -8.431607   -8.278664\n",
       "0      CRIM  -9.984341   -9.567877   -9.140572\n",
       "7       DIS -17.381223  -16.868997  -16.616306\n",
       "11    LSTAT -21.450356  -21.357121  -21.501518"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fs = pd.DataFrame({\"columns\":list(feat_names), \"coef_lr\":list((lr.coef_.T)), \"coef_ridge\":list((ridge.coef_.T)), \"coef_lasso\":list((lasso.coef_.T))})\n",
    "fs.sort_values(by=['coef_lr'],ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "第五题：\n",
    "Q:代码中给出了岭回归（RidgeCV）和Lasso（LassoCV）超参数（alpha_）调优的过程，请结合两个最佳模型以及最小二乘线性回归模型的结果，给出什么场合应该用岭回归，什么场合用Lasso，什么场合用最小二乘。\n",
    "\n",
    "A:\n",
    "特征相关性弱的时候使用最小二乘，相关性强的时候使用岭回归。\n",
    "lasso得到稀疏解，需要有特征选择的时候可以应用。\n"
   ]
  }
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